The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface

Vučković, Aleksandra; Hasan, Muhammad Abul; Osuagwu, B.A.; Fraser, Matthew; Allan, David B.; Conway, B.A.; Nasseroleslami, Bahman

doi:10.1016/j.clinph.2014.12.033

Cited by 25 publications

(12 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After about 15 CSP filters classification accuracy decreased. The general relationship observed here where accuracy increases and then decreases with increasing number of CSP filters is reported in another study [29]. At each number of CSP filter, statistical comparison showed no significant difference between the maximum accuracy for the eMI and each of the iMI combinations.…”

Section: B Classificationsupporting

confidence: 74%

Is Implicit Motor Imagery a Reliable Strategy for a Brain–Computer Interface?

Osuagwu

Zych

Vučković

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Explicit motor imagery (eMI) is a widely used brain-computer interface (BCI) paradigm, but not everybody can accomplish this task. Here, we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able-bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgment task is known to require mental rotation of a person's own hands, which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left hand and right hand, the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left hand and 78 ± 7% for the right hand, which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special BCI based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement, or for treatment of bodily spatial neglect.

show abstract

Section: B Classificationsupporting

confidence: 74%

Is Implicit Motor Imagery a Reliable Strategy for a Brain–Computer Interface?

Osuagwu

Zych

Vučković

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…The analysis did not detect any significant effect due to the order of the experiments and at the same time showed a very high effect size (partial η 2 ) and the probability of reproducibility (very high statistical power) for the force–position effect. Future studies with additional recordings such as electromyography (EMG; Laine, Martinez‐Valdes, Falla, Mayer, & Farina, ; Pizzamiglio, De Lillo, Naeem, Abdalla, & Turner, ) and electroencephalography (EEG; Nasseroleslami, Lakany, et al., ; Xu et al., ; Vuckovic et al., ), as well as the computational simulation of the potential neural (Nasseroleslami, Vossoughi, et al., ) and biomechanical (Rashedi, Khalaf, Nassajian, Nasseroleslami, & Parnianpour, ; Sedighi et al., ) factors, can be used to further elucidate the neurophysiological mechanisms giving rise to the behavioural specialization. Eventually, the findings from this and other studies need to be assessed with accurate reference to the limb (upper/lower, dominant/non‐dominant) and joint (proximal: shoulder, elbow; distal: wrist, hand) involved in the task, and measures used to quantify the skill level.…”

Section: Discussionmentioning

confidence: 99%

Visuomotor control of ankle joint using position vs. force

Farjadian

Nabian

Hartman

et al. 2019

Eur J of Neuroscience

View full text Add to dashboard Cite

Ankle joint plays a critical role in daily activities involving interactions with environment using force and position control. Neuromechanical dysfunctions (e.g., due to stroke or brain injury), therefore, have a major impact on individuals’ quality of life. The effective design of neuro‐rehabilitation protocols for robotic rehabilitation platforms relies on understanding the control characteristics of the ankle joint in interaction with external environment using force and position, as the findings in upper limb may not be generalizable to the lower limb. This study aimed to characterize the skilled performance of ankle joint in visuomotor position and force control. A two‐degree‐of‐freedom (DOF) robotic footplate was used to measure individuals’ force and position. Healthy individuals (n = 27) used ankle force or position for point‐to‐point and tracking control tasks in 1‐DOF and 2‐DOF virtual game environments. Subjects’ performance was quantified as a function of accuracy and completion time. In contrast to comparable performance in 1‐DOF control tasks, the performance in 2‐DOF tasks was different and had characteristic patterns in the position and force conditions, with a significantly better performance for position. Subjective questionnaires on the perceived difficulty matched the objective experimental results, suggesting that the poor performance in force control was not due to experimental set‐up or fatigue but can be attributed to the different levels of challenge needed in neural control. It is inferred that in visuomotor coordination, the neuromuscular specialization of ankle provides better control over position rather than force. These findings can inform the design of neuro‐rehabilitation platforms, selection of effective tasks and therapeutic protocols.

show abstract

“…Subactue incomplete patients may have stronger, parietally shifted, less lateralized cortical ERD, which becomes more central and lateralized over the course of recovery [5]. On the other hand, in patients with chronic complete SCI who did not recover motor function of their upper limbs, ERD is weaker than in able-bodied, typically resulting in worse BCI performance [51] unless affected by a secondary condition, such as a chronic pain [52] . Thus it is possible that uni-vs bimanual classifiers would have different performances in rehabilitative BCI used by incomplete subacute SCI patients and in assistive BCI used by chronic complete SCI patients.…”

Section: Discussionmentioning

confidence: 99%

Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces

Vučković

Pangaro

Putri

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B-L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits.

show abstract

The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface

Cited by 25 publications

References 49 publications

Is Implicit Motor Imagery a Reliable Strategy for a Brain–Computer Interface?

Is Implicit Motor Imagery a Reliable Strategy for a Brain–Computer Interface?

Visuomotor control of ankle joint using position vs. force

Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces

Contact Info

Product

Resources

About